Haldor Topsoe Combines Green Tech & Fossil Tech

A recent post to the Haldor Topsoe blog announced the following …

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By Svend Ravn, 24.05.2019
An article in the esteemed Science magazine suggests that as much as one percent of global CO2 emissions can be saved if electrified, and significantly more compact, technology is applied across the chemical industry.
The groundbreaking technology from Haldor Topsoe produces synthesis gas (syngas), an essential building block in the production of polymers and chemicals.
Researchers from Haldor Topsoe, Technical University of Denmark, Danish Technological Institute, and Sintex have been part of the development

The improved efficiency saves CO2 in itself, but the real gain comes from replacing natural gas with electricity for heating the process to the 900°C necessary. The full potential is achieved when using green electricity from wind turbines or solar panels.
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Read the entire post at: https://blog.topsoe.com/article-in-science-extremely-compact-reactor-has-potential-to-reduce-global-co2-emissions-significantly?utm_source=hs_email&utm_medium=email&utm_content=73013263&_hsenc=p2ANqtz-8RYl_VhLe6AihXk0WTQiBkht0vMso41uvytfX0WrNpfC-NFNQPrXcNz6KD1dLUs6N9iEOs2gq8L_i8lnzOzfFFOxxaLw&_hsmi=73013263

TIP: Subscribe to Haldor Topsoe’s blog (https://www.topsoe.com/content/blog)

Intriguing as it is, the post never mentions how to find the Science article cited.

TIP: Since press releases and blog posts often omit citation details, Google® the name of the publication and a few key words from the title to find the source mentioned in the announcement. That enables you to access the original article.

For example, I entered the following search string in a Google® search …
science magazine extremely compact reactor

I found …

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Electrified methane reforming: A compact approach to ... - Science
https://science.sciencemag.org/content/364/6442/756
22 hours ago - Electrified methane reforming: A compact approach to greener .... (D) Axisymmetrical reactor cross-section, outlining the most relevant .... This is an article distributed under the terms of the Science Journals Default
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Clicking on the link provides the author names and an abstract. You would have to pay for the full article. However, even the abstract by itself offers information supplementing the text of the Haldor Topsoe post.

NOTE TO NEW READERS of the DESULFURIZATION BLOG
www.desulf.blogspot.com

The blog name may be misleading. I chose it because of my background as a librarian in the oil & gas industry.

The main purpose of the blog is to offer you tips and tricks on how to maximize your efficiency when researching technology online.

Conference Alert: EmTech Next 2019

MIT Media Lab (https://events.technologyreview.com/emtech/next/19/) is staging an AI-Artificial Intelligence event, June 11-12, 2019, in Cambridge, Massachusetts.

EmTech Next promises to address …

• Artificial Intelligence and its impact on businesses
• Advances in human-robot collaboration
• Leadership in an era of constant reinvention
• Technologies that bring the digital factory to life
• How AR/VR is changing the enterprise training landscape
• Jobs of the future

Visit (https://events.technologyreview.com/emtech/next/19/) for more details.

TIP: Consider subscribing to the MIT online newsletters. All of them touch on AI. One is specifically dedicated to developments in the world of Artificial Intelligence. Visit https://go.technologyreview.com/newsletters  for more details.

Five questions that cut through AI hype

MIT produces a number of online newsletters, including one focusing on AI-Artificial Intelligence. Excerpts from a recent newsletter appear below.

In particular, I was interested in the author’s five-item checklist for AI projects.

Briefly, the checklist, paraphrased by me, is …

• State the problem
• How can machine learning solve the problem?
• Source the training data
• Audit the algorithms
• Should you – even if you could – use machine learning to solve the problem?

But take a minute to go to the horse’s mouth. Here are excerpts …

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Five questions you can use to cut through AI hype
Here’s a checklist for assessing the quality and validity of a company’s machine-learning product.
by Karen Hao
May 15, 2019
Two weeks ago I was in Dubai attending Ai Everything, the United Arab Emirates' first major AI conference and one of the largest AI applications conferences in the world. The event was an impressive testament to the breadth of industries in which companies are now using machine learning. It also served as an important reminder of how the business world can obfuscate and oversell the technology’s abilities.
In response, I’d like to briefly outline the five questions I typically use to assess the quality and validity of a company’s technology:

1. What is the problem it’s trying to solve? I always start with the problem statement. What does the company say it’s trying to do, and is it worthy of machine learning? Perhaps we’re talking to Affectiva, which is building emotion recognition technology to accurately track and analyze people’s moods. Conceptually, this is a pattern recognition problem and thus would be one that machine learning could tackle (see: What is machine learning?). It would also be very challenging to approach through another means because it is too complex to program into a set of rules.
2. How is the company approaching that problem with machine learning? Now that we have a conceptual understanding of the problem, we want to know how the company is going to tackle it. An emotion recognition company could take many approaches to building its product. It could train a computer vision system to pattern match on people’s facial expressions or train an audio system to pattern match on people’s tone of voice. Here, we want to figure out how the company has reframed its problem statement into a machine-learning problem, and determine what data it would need to input into its algorithms.
3. How does the company source its training data? Once we know the kind of data the company needs, we want to know how the company goes about acquiring it. Most AI applications use supervised machine learning, which requires clean, high-quality labeled data. Who is labeling the data? And if the labels are subjective like emotions, do they follow a scientific standard? In Affectiva’s case you would learn that the company collects audio and video data voluntarily from users, then employs trained specialists to label the data in a rigorously consistent way. Knowing the details of this part of the pipeline also helps you identify any potential sources of data collection or labeling bias (See: This is how AI bias really happens).
4. Does the company have processes for auditing its products? Now we should examine whether the company tests its products. How accurate are its algorithms? Are they audited for bias? How often does it re-evaluate its algorithms to make sure they’re still performing up to par? If the company doesn’t yet have algorithms that reach its desired accuracy or fairness, what plans does it have to make sure they will before deployment?
5. Should the company be using machine learning to solve this problem? This is more of a judgement call. Even if a problem can be solved with machine learning, it’s important to question whether it should. Just because you can create an emotion recognition platform that reaches at least 80% accuracy across different races and genders doesn’t mean it won’t be abused. Do the benefits of having this technology available outweigh the potential human rights violations of emotional surveillance? And does the company have mechanisms in place to mitigate any possible negative impacts?
In my opinion, a company with a quality machine-learning product should check off all the boxes: they should be tackling a problem fit for machine-learning, have robust data acquisition pipeline and auditing processes, have high accuracy algorithms or a plan to improve them, and be grappling head-on with ethical questions. Oftentimes, most companies pass the first four tests but not the last. For me, that is a major red flag. It demonstrates that the company isn’t thinking holistically about how its technology can impact people’s lives and has a high chance of pulling a Facebook later down the line. If you’re an executive looking for machine-learning solutions for your firm, this should warn you against partnering with a particular vendor.
source: https://www.technologyreview.com/s/613535/five-questions-you-can-use-to-cut-through-ai-hype/
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TIP: Visit AI Everything (https://ai-everything.com/). Here is some of what you will find on the Web site …

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AI Everything Summit for Governments & Businesses | 10 - 11 March 2020, Dubai
The Year's Most Anticipated & Empowering AI Summit for Governments, Businesses, Social Enterprises & the Creative Economy. 10 - 11 March 2020, Dubai
About AI Everything
The global AI phenomenon will create a new world order, and the UAE Strategy for Artificial Intelligence 2031 envisions the country to become a world leader in AI by 2031, creating opportunities and generating up to $90 billion in extra growth.

At AI Everything, the year's most anticipated AI summit, we set out to unite the divided conversations in AI.
source: https://ai-everything.com/